Predicting the apolipoprotein E ε4 allele carrier status based on gray matter volumes and cognitive function

Abstract Background Apolipoprotein E (ApoE) ε4 carriers have a higher risk of developing Alzheimer's disease (AD) and show brain atrophy and cognitive decline even before diagnosis. Objective To predict ApoE ε4 status using gray matter volume (GMV) obtained from magnetic resonance imaging images and demographic data with machine learning (ML) methods. Methods We recruited 74 participants (25 probable AD, 24 amnestic mild cognitive impairment, and 25 cognitively normal older people) with known ApoE genotype (22 ApoE ε4 carriers and 52 noncarriers) and scanned them with three‐dimensional (3D) T1‐weighted (T1W) and 3D double inversion recovery (DIR) sequences. We extracted GMV from regions of interest related to AD pathology and used them as features along with age and mini–mental state examination (MMSE) scores to train different ML models. We performed both receiver operating characteristic curve analysis and the prediction analysis of the ApoE ε4 carrier with different ML models. Results The best model of ML analyses was a cubic support vector machine (SVM3) that used age, the MMSE score, and DIR GMVs at the amygdala, hippocampus, and precuneus as features (AUC = .88). This model outperformed models using T1W GMV or demographic data alone. Conclusion Our results suggest that brain atrophy with DIR GMV and cognitive decline with aging can be useful biomarkers for predicting ApoE ε4 status and identifying individuals at risk of AD progression.


INTRODUCTION
Dementia is a major public health issue that affects millions of people worldwide.Alzheimer's disease (AD) is the most common form of dementia and is characterized by progressive cognitive impairment and brain atrophy (Breijyeh & Karaman, 2020;Molinder et al., 2021).
Apolipoprotein E (ApoE) ε4 allele is the major genetic risk factor for AD and is associated with increased amyloid-β (Aβ) deposition and accelerated brain atrophy (Corder et al., 1993;Suzuki et al., 2020).ApoE ε4 carriers have a higher risk of developing AD and show cognitive decline even before diagnosis (Corder et al., 1993).Therefore, it is important to identify ApoE ε4 carriers among older individuals who may have a high risk of AD progression.
Although the standard method to obtain ApoE genotype information is using blood samples, an alternative approach is to use brain imaging techniques to evaluate brain atrophy as a biomarker for ApoE ε4 status.Magnetic resonance imaging (MRI) is a widely used technique to measure gray matter volume (GMV) in regions-of-interest (ROI) related to AD pathology.However, different MRI sequences may have different sensitivities and specificities for detecting GMV changes.
In this study, we investigated two MRI sequences: three-dimensional (3D) T1-weighted (T1W) and 3D double inversion recovery (DIR) (Jahng et al., 2016).Although 3D T1W is routinely used to evaluate GMV changes, the 3D DIR sequence has been shown to suppress both cerebrospinal fluid (CSF) and white matter signals more effectively than the T1W sequence, which may reduce the partial volume effect and improve the accuracy of GMV quantification (Jahng et al., 2016).Furthermore, based on this characterization, DIR could obtain whole-brain-high-spatial resolution 3D images with clearly delineating cortical and deep gray matter structures (Pouwels et al., 2006).
In addition, DIR could improve the signal-to-noise ratio and enhance lesion contrast to increase the description of lesions within the cerebral cortex as well as increase definition when evaluating mixed white matter-gray matter lesions (Eichinger et al., 2017;Geurts et al., 2005).
Machine learning (ML) methods have been widely applied to neuroimaging data to classify or predict various neurological conditions.ML methods can combine multiple types of information, such as brain images and demographic data, to improve the accuracy and reliability of the predictions.By using a multiple-kernel support vector machine (SVM), multiple types of information can be combined and compared with a single biomarker using single-kernel learning.The prediction performance of this multiparametric approach may be higher than that of a single-factor approach when classifying ApoE ε4 carriers from noncarriers.We developed a prediction method using an ML-based optimized combination feature (OCF) set on GMV to classify and predict older cognitively normal (CN) participants, amnestic mild cognitive impairment (MCI), and mild and moderate AD patients (Kim et al., 2020).This OCF-based method showed effective prediction performance for the MCI stage (Kim et al., 2020).This technique could be used to predict ApoE ε4 status with GMV and demographic parameters related to cognitive function.Few studies have evaluated the prediction of ApoE ε4 status with both GMV and demographic parame-ters such as age and the mini-mental state examination (MMSE) score (Honea et al., 2009;Kim et al., 2013;Yim et al., 2022).ApoE ε4 carriers are thought to show a close relationship between decreased cognitive function and brain atrophy, regardless of whether they are AD patients or not.
We hypothesized that the prediction of the ApoE ε4 carrier may be improved with DIR images due to the increase in sensitivity.Furthermore, combining MRI data and demographic data would improve the prediction of ApoE ε4 status compared to using either data source alone.To test our hypothesis, we recruited AD, amnestic MCI, and CN older participants with known ApoE genotypes and scanned them with both T1W and DIR sequences.In addition to MRI sequences, we also considered demographic data such as age and MMSE score as potential predictors of ApoE ε4 status.Age and MMSE score are known to be correlated with cognitive function and brain atrophy (Pini et al., 2016).
We extracted GMV from ROIs related to AD pathology and used them as features along with age and MMSE score to train different ML models.We evaluated the models using receiver operating characteristic (ROC) curve analysis.The main objective of this study was to investigate the prediction of the ApoE ε4 status using GMV obtained from T1W or DIR images and/or demographic data with ML methods.The specific aims were as follows: (1) to evaluate the group classifications between ApoE ε4 carriers and noncarriers using ROC curve analysis with GMV obtained from T1W or DIR images and/or demographic data; (2) to compare the performance of different ML models for predicting ApoE ε4 status using different combinations of features.

Participants
This study was approved by our institutional review board, and informed consent was obtained from all participants.Participants were prospectively recruited in the Brain Neurological Center of our institution.All participants were Korean.A detailed medical history was provided by them, and they underwent a neurologic examination, standard neuropsychological testing, genotype evaluation, and MRI scan.
A neuroradiologist with 12 years of imaging experience evaluated the brain MR images for each participant to determine any evidence of prior cortical infarctions or other space-occupying lesions.
A standardized neuropsychological battery in Korea that covers the five cognitive subsets: attention, memory, language, visuospatial function, and frontal/executive function was used to assess cognitive function (Ahn et al., 2010).et al., 2007;McKhann et al., 1984).They were defined as those with clinical dementia rating scores of 0.5, 1, or 2 based on the results of the neuropsychological examination.In addition, amnestic MCI subjects were included in the study based on the Petersen criteria (Petersen, 2016;Petersen et al., 1999Petersen et al., , 2001)).Finally, CN older participants were selected from healthy volunteers who did not have a medical history of neurological disease, who showed normal results on detailed eval-

MRI acquisition
MR images were obtained with a 3 Tesla MRI scanner (Achieva, Philips Medical System) with an 8-channel head coil.Both 3D T1W and 3D DIR sequences were scanned for each participant to determine their GMVs.The isotropic sagittal structural 3D T1W images (T1WIs) were acquired from the turbo-field echo sequence with the following imag-

Preprocessing of images
The following preprocessing analysis was performed using Statistical Parametric Mapping-version 12 (SPM12) software (Wellcome Department of Imaging Neuroscience, University College, London, UK).First, brain tissue volumes were obtained by segmenting both 3D DIR and 3D T1WIs.Second, study-specific templates for 3D T1W and 3D DIR were created separately using segmented brain tissue volume maps and the "Diffeomorphic Anatomical Registration Through Exponentiated Lie Algebra (DARTEL)" algorithm (Ashburner, 2007).Third, the segmented GMV maps of both 3D DIR and 3D T1WIs were applied to the study-specific DIR and T1W templates, respectively.Finally, the spatially normalized GMV maps for both DIR and T1W were smoothed using a Gaussian kernel of 8 × 8 × 8 mm full width at half maximum to perform the following voxel-based statistical analyses.

Demographic data and clinical outcome scores
Age and MMSE scores were compared between ApoE ε4 carrier and noncarrier groups using the two-sample t-test.The chi-squared test was used to compare sex.

2.4.2
Voxel-based analysis of GMV map A voxel-based two-sample t-test was applied with the participant's age as a covariate to compare GMVs between the ApoE ε4 carrier and noncarrier groups for each sequence.The significance level was set at α = .05with correcting multiple comparisons using the false discovery rate method and with the minimum cluster size with at least 100 contiguous voxels.This study was mainly performed to define the specific brain areas for the ROI analysis.

ROI-based analysis of GMV value
GMV values for each participant and each sequence were obtained by defining ROIs using the following two different methods: First, ROIs were defined at the disease-specific brain areas such as the amygdala, hippocampus, and precuneus.Second, additional ROIs were defined in the areas of significant differences in GMV between the two groups on the voxel-based analysis, such as the posterior cingulate, middle frontal gyrus (MFG), and middle temporal gyrus (MTG).These areas were automatically traced on the brain atlas space using the WFU_PickAtlas software toolbox (fmri.wfubmc.edu/software/pickatlas).The mean value of GMV for each participant and each sequence was obtained from the six ROIs using the Marsbar software (Matthew Brett, http:// marsbar.sourceforge.net).
The following three analyses were performed using ROI data.First, a two-sample t-test was performed to compare GMV between ApoE ε4 carrier and noncarrier groups for each ROI area and each sequence.
Second, a ROC curve analysis was performed for each sequence to evaluate the differentiation between ApoE ε4 carriers and noncarriers using the demographic data of age, gender, or MMSE scores and/or GMV for each ROI area.Furthermore, ROC curves between T1W and DIR were compared for each ROI.For those ROI analyses, α < .05 was used to determine the significance level.Third, a logistic regression analysis was performed to evaluate the association between ApoE4 status and MMSE scores or GMVs from the T1WI or DIR technique for each ROI.The statistical analysis was performed using the MedCalc statistical software (http://www.medcalc.org/).Finally, ML methods were used to predict ApoE ε4 carriers and noncarriers using the following two-step analyses.In the first step, the features were selected using the OCF setting method to minimize the curse of dimensionality from nine parameters of age, sex, MMSE, and GMV values from six ROIs for each sequence (Kim et al., 2020).The number of combinations was estimated to be a combination of features from a minimum of single to a maximum of nine from all features.The total combination set for the combination-based feature selection was calculated as 511 based on the equation nC r (n = 9 and 1 ≤ r ≤ 9), where n represents the number of features, and r represents the number of features being chosen at a time.In the next step, three different SVM kernels which are the linear kernel (first-order polynomial SVS or SVM1), quadratic kernel (second-order polynomial SVM or SVM2), cubic kernel (thirdorder polynomial SVM or SVM3) as well as the two additional models of Bootstrap-Aggregated decision tree or Tree Bagger (TB) (Meinshausen, 2006) and kernel-based naive Bayes (NB) (Manning et al., 2008) models were used to train the SVM kernel classifiers to predict the ApoE ε4 carriers and noncarriers groups using each OCF set.The dataset was randomly evaluated for the test dataset using a threefold crossvalidation technique.The result of this analysis was presented by the area under curve (AUC) calculations for each OCF set and with each kernel type from threefold cross-validation sets.The ratio between the training set and the test set was 7-to-3.Furthermore, we performed the three-group classification with the ML models of first-, second-, and third-order SVM, TB, or NB using features of age, sex, MMSE scores, ApoE state, and all ROIs' GMV for each sequence.In this analysis, we also used the threefold cross-validation sets.

Demographic data
The carrier group was significantly older than the noncarrier group (p = .042)(Table 1).The MMSE scores were significantly lower in the carrier group than in the noncarrier group (p = .001).No significant difference was found between the two groups in sex (χ 2 = .026,p = .872).More female than male participants were observed for both groups.

Voxel-based comparison
Significant GMV reductions in both DIR and T1WIs were observed in the ApoE ε4 carrier group compared with the noncarrier group (Figure 1).The areas of the GMV reduction in both sequences were precuneus, frontal gyrus, and temporal gyrus (Table S1).Compared to T1W, the areas of GMV loss in the DIR were extended to the anterior cingulate, posterior cingulate, and parahippocampal gyrus.

Prediction analysis with machine learning
Table 3 shows the prediction result using ML analysis.AUC values with DIR GMVs were higher than those with T1W GMVs.

3.3.3
Three-group classification using ML models for each sequence Table 5 lists the results of the three-group classification with the different ML models using both demographic data and GMV values obtained from six different brain areas for each imaging sequence.The largest AUC values were .763for T1W GMV with the first-order SVM model and .830for DIR GMV with the third-order SVM model.The largest AUC values listed in this table for each sequence were always obtained using 10 features of the combination of age, sex, MMSE scores, ApoE state, and 6 ROIs' GMV.Therefore, we used all 10 features for both sequences.Because SVM3 shows the largest AUC value, we show the confusion matrix of SVM3 for both T1W GMV and DIR GMV in Supplementary Figure S1 to show the model performance.For T1W GMV, the correct predictions were 60% (15/25) for CN, 45.8% (11/24) for MCI, and 72% (18/25).One CN participant was predicted as AD and two AD participants were predicted as CN.For DIR GMV, the correct predictions were 68% (17/25) for CN, 83.3% (20/24) for MCI, and 80% (20/25).All CN participants were predicted as CN or MCI and three AD participants were predicted as CN.

GMV reduction in APOE ε4 carriers
Both DIR and T1W techniques determined that GMV was significantly reduced in ApoE ε4 carriers relative to noncarriers in several brain areas (Figures 1 and 2) which was a consistent finding in previous reports (Bailey et al., 2015;Coughlan et al., 2021;Hashimoto et al., 2001;Liu et al., 2010) 4).Furthermore, ApoE ε4 was negatively associated with GMV with both T1WI and DIR.DIR GMV was statistically significant in more ROIs compared with T1WI GMV, which indicates that the DIR sequence was more sensitive.Therefore, DIR may have an advantage over T1W in evaluating the characteristics of the ApoEε4 carriers by evaluating GMV loss in the brain.

Prediction of apolipoprotein E ε4 allele carriers
The ML method showed that the highest AUC value was obtained when the following four features were included in the prediction analysis with the cubic kernel SVM (SVM3) model: DIR GMVs in the TA B L E 3 Prediction analysis results using the machine learning models with the demographic data and the gray matter volume (GMV) from the two different magnetic resonance imaging (MRI) sequences to differentiate between apolipoprotein E (ApoE) ε4 carriers and noncarrier groups.than those without ε4 (Nicoll et al., 2011;Vemuri et al., 2010).A previous study showed that the risk of AD increased almost four times as the number of ApoE ε4 alleles in families with late-onset AD increased, whereas the mean age at onset decreased from 84 to 68 years (Corder et al., 1993;Verghese et al., 2011).In particular, a more than 10-fold increase in the risk of older people developing late-onset familial and sporadic AD was caused by ApoE ε4 homozygotes compared to that due to other mutations (Rhinn et al., 2013).Previous studies have shown significant volume loss in the hippocampus and amygdala in homozygous ε4 carriers with AD compared with noncarriers (Filippini et al., 2009;Liu et al., 2010;Tanaka et al., 1998).However, few studies have been performed to evaluate the prediction of an ApoE ε4 carrier with images (Honea et al., 2009;Kim et al., 2013;Yim et al., 2022).

Model
The classification of AD from CN was investigated using a combination of fluorodeoxyglucose positron emission tomography, structural MRI, CSF protein levels, and apolipoprotein-E (ApoE) genotype with an SVM ML method (Gupta et al., 2019).In addition, the contribution of ApoE 4 to modify the associations between GMV and cognitive impairment was investigated, suggesting a shift in the positive association between GMV and cognitive performance due to cholinergic neuronal dysfunction resulting from ApoE 4 status (Cacciaglia et al., 2019).The age at onset of AD and neuropathologic progression (Lahiri et al., 2004) and the rate of cognitive decline (Martins et al., 2005) were predicted by the ApoE genotype.Our finding is supported by results of previous studies that ApoE 4 existence can be predicted with GMV loss and cognitive impairment.
In addition, the results of the ROC curve analysis showed that the AUC value was .758when both age and MMSE scores were combined as features ( faster and the high impact index can be picked up easier by it than ROC analysis. Results of the three-group classification with the different ML models showed that the largest AUC values were .830for DIR GMV and .763for T1W GMV (Table 5).Although AUC values were different among the ML models, the classification among the three participant groups was better DIR GMV than T1W GMV, indicating that GMV obtained with a DIR sequence can be used to differentiate the participant groups.

Study limitations
This study had some limitations.First, the number of participants was relatively small, especially the ApoE carrier group.Therefore, our results need to be validated by performing this study with a relatively large population.Second, all participants enrolled in our study were Korean.Due to the different effects of AopE ε4 status on different demographic distributions (Farrer et al., 1997), our study may be difficult to cover diverse cohorts.It is necessary to investigate the association between ApoE ε4 status and GMV in other demographic cohorts in future studies.Third, a prediction analysis was performed using the ML method with threefold cross-validation.This validation can be increased up to 10 folds with increasing the number of participants.Finally, the MMSE score was used for cognitive function in this study.Other cognitive functions, such as attention, memory, language, visuospatial function, and frontal/executive function, may need to be investigated.
The carrier group had significantly lower GMV than the noncarrier group in the bilateral amygdala, right precuneus, and right MTG for both T1W and DIR.Only the DIR image showed additional areas of significant GMV reduction in the carrier group in the bilateral hippocampus, the left precuneus, the bilateral MFG, and the left MTG.Second, the results of the logistic regression analysis between ApoE state and MMSE scores or GMVs from T1WI or DIR sequence were listed in Table 4. Increased MMSE score was associated with a reduced odds ratio (OR) of the F I G U R E 1 Results of the voxel-based two-sample t-test analyses of gray matter volume (GMV) between the two groups of apolipoprotein E (ApoE) ε4 carrier and noncarrier for three-dimensional T1 (3DT1) and double inversion recovery (DIR) sequence.ApoE4-carrier (OR = .876,p = .003).With T1WIs, more GMV loss was associated with increased OR of ApoE4-carrier in left amygdala (OR = .889,p = .020),right amygdala (OR = .849,p = .013),right precuneus (OR = .788,p = .007),and MFG (OR = .852,p = .020).
The highest AUC value was obtained with the cubic SVM (SVM3) model by combining the five features of age, MMSE score, and DIR GMVs at the amygdala, hippocampus, and precuneus (AUC = .88).The second highest AUC value was obtained with the quadratic SVM (SVM2) model by combining the four features of age, MMSE score, and DIR GMVs at the hippocampus and MTG (AUC = .84).Without using any demographic data of age, MMSE score, and sex, the DIR GMVs combining both amygdala and precuneus provided AUC = .78with the linear SVS (SVM1) model.With T1W GMVs, the AUC values were .81for the TB model by combining the three features of sex, age, and T1W GMVs at the amygdala and .81for the NB model by combining the four features of age, MMSE score, and T1W GMVs at the amygdala, and hippocampus.

F I G U R E 2
Results of the two-sample t-test analyses of the gray matter volume (GMV) value between the two groups of apolipoprotein E (ApoE) ε4 carrier and noncarrier for three-dimensional T1-weighted (T1W) and double inversion recovery (DIR) sequence at each brain area.Brain region-of-interest (ROI) areas were the amygdala, hippocampus, precuneus, posterior cingulate, middle frontal gyrus (MFG), and the middle temporal gyrus (MTG) at the right (Rt) or left (Lt) side.induces susceptibility artifacts.However, DIR is a spin-echo sequence, which is less affected by field inhomogeneity artifacts.Gray matter segmentation may also be affected by B0 field inhomogeneity in the frontal and temporal areas.Therefore, gradient-echo T1W may underestimate GMV in the temporal area but overestimate in the frontal area of the brain (Diaz-de-Grenu et al., 2011).Furthermore, white matter signals are reduced in DIR images by suppressing both CSF and white matter.ApoE ε4 state showed a negative association with MMSE scores (Table

Table 2
except the posterior cingulate significantly differentiated the carrier group from the noncarrier group.The AUC value was improved by combining GMV and the demographic data of both age and MMSE scores in each sequence, but it was similar to that of the combination of both age and MMSE scores.No significant difference was found between DIR GMV and T1W GMV for all ROIs in AUC values.
Results of receiver operating characteristic (ROC) curve analyses with the demographic data or gray matter volume (GMV) for the group classifications between apolipoprotein E (ApoE) ε4 carrier and noncarrier. of age, sex, and MMSE score and GMV at the region-of-interest (ROI) areas for each sequence were used for the receiver operating characteristic (ROC) analyses.
. The carrier group showed GMV reduction by DIR in the bilateral hippocampus, bilateral MFG, and bilateral MTG, but was not by gradient-echo T1W, indicating that DIR was more sensitive than T1W in detecting GMV loss.The 3D T1W TFE sequence, which is similar to the MPRAGE technique, is a gradient-echo sequence that is usually affected by inhomogeneity in an applied magnetic field that TA B L E 2 Abbreviations: AUC, area under the ROC curve; DIR, double inversion recovery; Lt, left; MFG, middle frontal gyrus; MMSE, mini-mental state examination; MTG, middle temporal gyrus; PC, posterior cingulate; Rt, right; T1W, T1-weighted.a The demographic data b Comparison of ROC curves between T1W and DIR.
Note: Prediction models: SVM1, the first-order SVM or linear SVM model; SVM2, the second-order polynomial SVM model or the quadratic SVM; SVM3, the third-order polynomial SVM or cubic SVM or random forest model; TB, the Bootstrap-Aggregated decision tree or Tree Bagger model; NB, the kernel-based naive Bayes or kernel-naive Bayes model.Num is the number of the total combined feature selection.One (1) indicates the selected parameter to calculate AUC, but zero (0) does the unselected parameter.Abbreviations: AUC, area under the ROC curve; DIR, double inversion recovery; MFG, middle frontal gyrus; MMSE, mini-mental state examination; MTG, middle temporal gyrus; PC, post cingulate; SVM, support vector machine; T1W, T1-weighted.Results of logistic regression analysis between apolipoprotein E (ApoE) state and the mini-mental state examination (MMSE) scores or gray matter volume (GMV) values for each three-dimensional (3D) T1-weighted image (T1WI) or double inversion recovery (DIR) sequence in each region-of-interest (ROI).The Korean version of the mini-mental state examination (MMSE) scores and gray matter volume (GMV) values at the region-of-interest (ROI) areas for each sequence were used for the logistic regression analysis with apolipoprotein E (ApoE) state.In this table, we list the result of odds ratio (OR) and 95% confidence interval (CI) with p-value for each analysis.Abbreviations: Lt, left; MFG, middle frontal gyrus; MTG, middle temporal gyrus; PC, posterior cingulate; Rt, right.amygdala,hippocampus,and precuneus as well as demographic values of ages and MMSE scores (AUC = .88)(Table3).Larger AUC values were obtained with DIR GMVs than T1W GMVs.High AUC values were obtained when age and MMSE scores were included as features.High AUC values were also obtained when GMVs in both the amygdala and hippocampus were included as features.With T1W GMV, higher AUC values were shown by the ML methods of TB and NB than any ker-

Table 2
Results of three-group classification with the different machine learning (ML) models using both demographic data and gray matter volume (GMV) values obtained from six different brain areas for each imaging sequence.The largest area-under-curve (AUC) values listed in this table were always obtained using 10-feature combination, which are age, sex, minimental state examination (MMSE) scores, apolipoprotein E (ApoE) state, and 6 region-of-interest (ROI)s' GMV values for each sequence.Therefore, we used all 10 features for both sequences.Features of the demographic data included in this analysis were age, sex, MMSE scores, and ApoE state.GMV values were used in the mean value if ROI had both the right and left sides.Machine learning (ML) models: support vector machine (SVM)1, the first-order SVM or linear SVM model; SVM2, the second-order polynomial SVM model or the quadratic SVM; SVM3, the third-order polynomial SVM or cubic SVM or random forest model; TB, the Bootstrap-Aggregated decision tree or Tree Bagger model; NB, the kernel-based naive Bayes or kernel-naive Bayes model.Abbreviations: DIR, double inversion recover; T1W, T1-weighted.
).Including GMVs in the amygdala, hippocampus, or MTG with the demographic values of both age and MMSE scores did not improve AUC value a lot (AUC = .747-.760), indicating that age and MMSE score are important predictors.The ML method is more helpful to evaluate predictions because multiple combinations can be analyzed TA B L E 5